Related Experiment Video
Updated: Jul 14, 2026
12:39
A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
Published on: January 18, 2020
7.7K
"Gloppiness" Phenomena and a Computer Vision Method to Quantify It.
Shijian Wu1, Mark Mintel1,2, Baran Teoman1
1Colgate-Palmolive Company, Piscataway, NJ 08854, USA.
Gels (Basel, Switzerland)
|July 28, 2023
Summary
We developed a fast, affordable computer vision method using Python to measure "gloppiness" in gels and fluids. This technique, based on jetting flow rupture time, correlates well with human perception, unlike traditional rheology.
Area of Science:
- Materials Science
- Fluid Dynamics
- Computer Vision
Background:
- The phenomenon of "gloppiness" in complex fluids and gels is prevalent but difficult to quantify objectively.
- Traditional rheology measurements show limited correlation with perceived "gloppiness".
Purpose of the Study:
- To develop a rapid, cost-effective computer vision method for quantifying "gloppiness" in complex fluids and gels.
- To assess the correlation between the proposed method and human perception of "gloppiness".
Main Methods:
- A Python-driven computer vision approach was implemented.
- Jetting flow rupture time was used as a key metric for measuring "gloppiness" in laboratory-produced shower gel samples.
- Results were correlated with data from a technical insight panelist team.
Main Results:
- The computer vision technique demonstrated a significant correlation with the perceived level of "gloppiness".
- Rheology measurements from commercial shear rheometers did not show a strong correlation with "gloppiness".
Conclusions:
- A Python-based computer vision technique utilizing jetting flow offers a promising, efficient, and affordable solution for assessing "gloppiness".
- This method provides a valuable tool for the industry to evaluate liquid and gel products.
